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ISSN: 0975-766X Available Online through Research Article
www.ijptonline.com COMPUTER-AIDED DESIGN AND SCREENING OF CHALCONES AS
NOVEL PPAR GAMMA AGONISTS Archana Kumari. D. N. S. S.*,1, Manga Ratnam. B1, Arun Kumar Kuna2, Srinivasulu. D3
1Avanthi Institute of Pharmaceutical Sciences, Cherukupally (V), Bhogapauram (M), Near Tagarapuvalasa Bridge, Vizianagaram - 531162, India.
2Vels College Of Pharmacy, Velan Nagar, P.V.Vaithiyalingam Road, Pallavaram, Chennai – 600117, India.
3University College Of Pharmaceutical Sciences - Andhra University, Visakhapatnam-530003, India.
E-mail: [email protected],
Received on 25-12-2009 Accepted on 15-01-2010
Abstract
Type II diabetes mellitus is a chronic metabolic disorder and PPARs were found to be better
targets in lowering glucose levels. Here, we report a computer-aided drug design approach to screen
various chalcones, designed using substituted benzaldehydes and acetophenones from e-molecule
library, as possible PPAR gamma agonists using Molegro Virtual Docker software. Based on the
dock scores and molecular weight comparisons of top 50 compounds from a designed data set of
2500 chalcones, compound 17_37 displayed a dock score of -230.10 kcal/mol with a maximum of
11 H-bond interactions.
Keywords: chalcone, diabetes, PPARgamma, docking
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1. Introduction
Chalcone is an aromatic ketone that forms the central core for a variety of important
biological compounds. Chalcones are prepared by aldol condensation reaction between a
benzaldehyde and an acetophenone in the presence of sodium hydroxide as a catalyst. Compounds
with chalcone as backbone have been reported to exhibit a wide variety of pharmacological effects
and the presence of a reactive α,β-unsaturated keto function in chalcones is found to be responsible
for their activity, which may be altered depending on the type and position of the substituent on the
aromatic rings.1
Chalcones can also be prepared by claisen schimdt reaction and Allan Robinson’s reaction.
They are known to possess a wide variety of therapeutic uses where the (E)-4-
aminoalkylthiochalcones and (E)-4-amino alkoxychalcones exhibited good antibacterial property
against Staphylococcus aureus, Enterococcus faecalis and Bacillus subtilis.2,3 Chalcones belong to
the flavonoid family and it was reported that 2′-methoxy-3,4-dichlorochalcone, 2′-hydroxy-6′-
methoxy chalcone, 2′-hydroxy-3-bromo-6′-methoxy chalcone and 2′-hydroxy-4′,6′-dimethoxy
chalcone potently inhibit iNOS-catalyzed NO production by different cellular mechanisms and
found to possess anti-inflammatory activities.4,5 Moreover, chalcones possess anti-fungal activity
where some of them were found to exhibit specificity with some proteins of Saccharomyces
cerevisiae, Hansenula polymorpha and Kluyveromyces lactis, respectively.6,7 Compounds like 2′,4′-
dihydroxy-6′-methoxy-3′,5′-dimethylchalcone (DMC), isolated from the buds of Cleistocalyx
operculatus possess anti-tumour activity when tested on human cancer cells5,8 while some chalcones
demonstrated the ability to block voltage-dependent potassium channels.9 They are also
intermediates in the biosynthesis of flavonoids, which are substances widespread in plants and with
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an array of biological activities. Chalcone based aryloxypropanol amines were evaluated for their
anti-hyperglycemic activity in rat models10 and 3-nitro-2′-benzyloxychalcone has stimulated glucose
uptake when tested on adipocytes.11
Type II diabetes mellitus is a chronic metabolic disorder, characterized by dysfunctioning of
pancreatic beta cells associated with insulin resistance, if not controlled leads to macro and
microvascular disorders.12,13 Dipeptidyl peptidase IV,14-16 GLP-1,17 Glucokinase18 and PPARs
(Peroxisome Proliferator Activated Receptors)19-21 have been identified as potential targets of type II
diabetes. Among these, PPARs which are a group of nuclear receptors were found to be better
targets in lowering glucose levels along with lipids. They activate transcription factors of many
genes and exist in three isoforms α (alpha), β (beta) and γ (gamma), of which, PPAR gamma
agonists are known to improve insulin sensitivity. Several such agonists have so far been described
in literature. They include benzimidazole derivatives,22 docosahexaenoic acid derivatives,23 N-(2-
Benzoylphenyl)-L-tyrosine,24 aryl-tetrahydropyridines,25 carbamate-tethered aryl propanoic acids,26
thiazolidinediones27 and others.28 However, certain side effects have been identified with these
agents like congestive heart failure, edema, fluid retention and weight gain.29 Thus, there is still a
need to develop novel, selective PPAR gamma agonists with reduced side effects. Therefore, in this
paper, a computer-aided drug design approach was employed to screen various chalcones as possible
PPAR gamma agonists.
Synthesizing new compounds utilizing high-throughput screening are carried out at high cost
and are also time consuming. However, an alternative process represents screening small molecule
databases for novel compounds and docking them into the protein of interest followed by scoring the
poses. This has become increasingly important in the context of drug discovery. Hence, we report
designing and screening of novel chalcones as possible PPAR gamma agonists by extracting various
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substituted benzaldehydes and acetophenones from e-molecule library followed by docking with
PPAR gamma (PDB id: 2Q6S).
2. Methods
Receptor X-ray structure
The X-ray crystal structure of PPAR gamma having PDB (Protein Data Bank) code 2Q6S30
was selected as receptor model in this study. We used eMolecules31 chemical library to design
various chalcones and Molegro Virtual Docker to perform docking analysis.
eMolecules Database
The database contains nearly 8 million unique chemical structures from 22 million sources.
Searching can be done either by using text or by structure. Structure based searching involves sub-
structure search or exact structure search and searching by text can be initiated by valid data formats
or wild cards. A structure based search was employed to extract 50 different benzaldehydes and
acetophenones, given in Tables 1 and Table 2.
Table 1: Various substituted benzaldehydes extracted from e-molecules database.
O
R 1
R 2
R 3
R 4
R 5
S.NO R1 R2 R3 R4 R5
1 F F F F F
2 H H H CH2Br H
3 CH3 CH3 H H CH3
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4 OCH3 H OCH3 H H
5 OH Cl Cl H Cl
6 H Br H Br NH2
7 H H OCH3 Cl H
8 CH3 H H H CH3
9 H Br
O(CH2)4 CH3 OCH3 H
10 H H H H OCHF2
11 H H O
O
Br
H
12 H H OCHF2
OCH2CH3 H
13
N
NO
H H Br
H
14 H H O(CH2)5CH3 OCH2CH3 H
15 H
O
Cl
F
OCH3 H H
16 O
N
OCH3 H H H
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17 H H O
H H
18 O
O
Cl
Br
H Cl H
19 H Br H H O
20 H OCH3
O
O
H H
21 H H
OO
Cl
H H
22 H Br O
Br H
23 H Cl H H O
NO
24 H H O
N
O
H H
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25 H Br O
O
O
Br H
26 H Cl
OO
Br
OCH3 H
27 H
O
O
OCH3 H H
28 O
O
H H Br H
29 H H H O
H
30 H OCH2CH3 O
Cl
Br H
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31 O
O
O
H H Cl H
32 H H OCH2OCH3 H OCH2OCH3
33 H H O
FCl
Br H
34 Cl H H O
Si
Cl
35
H H O
O
F
36 H H
O
Cl
O
OCH2CH3 H
37 H OCH2CH3 O
F
Cl H
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38 H H H H N
N
39 H H H O
O
H
40 H H O
O
OCH2CH3 H
41
F
N
N
H H F H
42 OCH2CHCH2 OCH2CH2
OCH2CH2OH
H H H
43
OCH3 H OCH3
44 H O
O(CH2)5CH3 OCH3 H
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45 O
N
O
Br H Cl H
46 H H O
H H
47 H O
O
OCH3 H H
48 H H H H H
49
H H H OCH3
50
H O
O(CH2)5CH3 OCH3 H
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Table 2: Various substituted acetophenones downloaded from e-molecules database.
O
R 1
R 2
R 3
R 4
R 5
S.NO R1 R2 R3 R4 R5
1 H F F F F
2 H H F H OH
3 Cl Cl F H H
4 H CH3 CH3 CH3 H
5 H H NH-Si-(CH3)3 H H
6 CH3 CH3 CH3 CH3 CH3
7 H C(CH3)3 H C(CH3)3 OH
8 C6H5 H C6H5
9 H H (CH)2(CH2)3 CH3 H H
10 H H
NH
N
F H
11 H H
Cl
N
H H
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12 H H H
O
H
13
H H H
14 H H H
NN
F
H
15 H H
N
F H
16 H CH3 CH3 H OH
17
Cl
O
H H H H
18 CH3 H
H
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19 H H
ON
N
H H
20 OH H H H O
O
O
O
O
O
21 H H N
S
N
F
F
F
F
F
H H
22 H H OCH2CH3
N
H
23 OH
OH H H
24 H H
H H
25 H H N
N
O
H H
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26 H H
H H
27 H
N
H H
28 H H H N
N
N
H
29 H H OH
N
N
H
30 H H N
N
N
N
O
F
H H
31 H H
N
N
S
N
H H
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32 H H
N
N
O
O
Cl
H H
33 H H OH O
O
O
OH
34 H H
N N
N
H H
35 H
NNNN
H H H
36 H H
NN
NN
C l
H H
37 H H H N
N
N N
NN
H
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38 H H
Cl
NN
N
NN
H H
39 H N
N
N
B r
H H H
40 H H
N
ON
N
F
N
H H
41 H H
N
N
O
O
H H
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42 H H
NN
N
N
N
F
H H
43 H H
N
NN C l
H H
44 H H
NN
N N
O
H H
45 H H N
N
NN
N
H H
46 H H
O
N
O
OH
H H
47 H
N
S
N N
H H H
48 H H H H H
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49 H H H N
N
H
50
H H
N
OH
N
H
Molegro Virtual Docker
Molegro Virtual Docker is a docking analysis tool used to predict the protein-ligand
interactions. It determines the potential binding site of the target protein and lead candidates are
identified by a molecular docking algorithm called MolDock, which is based on a new search
algorithm that combines differential evolution with a cavity prediction algorithm.32 The scoring
scheme was derived from PLP (Piecewise Linear Potential) scoring functions originally proposed by
Gehlhaar et al33 and later extended by Yang et al.34 The scoring function was further improved to
include new hydrogen bonding term and charge schemes.
Data Set
The combinations of various substituted benzaldehydes and acetophenones were transformed
into 2500 chalcones using ISIS draw software. Before docking, an energy minimization routine was
performed to generate three dimensional structures of all the molecules using corina make 3D
option, derived charges and the geometries were optimized using cosmic module of Tsar software
and exported them as sybyl mol2 files. Water molecules were discarded from the PDB file,
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hydrogens were added and docking was performed using MolDock docking engine of Molegro
software. The binding site was defined as a spherical region which encompasses all protein atoms
within 15.0 Å of bound crystallographic ligand atom (dimensions X (38.52 A°), Y (31.61 A°), Z
(42.08 A°) axes, respectively). Default settings were used for all the calculations. Docking was
performed using a grid resolution of 0.3 Å and for each of the 10 independent runs; a maximum
number of 1000 iterations were executed on a single population of 50 individuals.
3. Results and Discussion
Before screening e-molecule library, the docking protocol was validated. 2Q6S protein
bound ligand was docked into the binding pocket to obtain the docked pose and the RMSD of all the
atoms between these two conformations is 0.71 A° (dock score of -159.57 kcal/mol), indicating that
the parameters for docking simulation are good in reproducing the X-ray crystal structure and can be
extended to search the new ligand binding conformations.
Various combinations of benzaldehyde and acetophenone moieties resulted in nearly 2500
chalcones with varied structural complexity that eventually reflected in their respective dock scores,
varying from -27.06 to -230.10 kcal/mol, respectively. Hence, keeping in view, the original ligand
dock score (-159.57 kcal/mol), the compounds were segregated based on their molecular weights.
The main emphasis is to extract chalcones with reasonable molecular weights, which have the ability
to pass through cell membranes35 as well as to identify best combinations that represent PPAR
gamma agonists. Therefore, given in Table-3 are dock scores and molecular weight comparisons of
top 50 compounds from a designed data set of 2500 chalcones, and the 2D structures are given in
Table-4. From Table-3 it is evident that the molecular weight Vs dock score comparisons resulted in
gradual increase of dock scores from 189.27 kcal/mol (400-450 KDa) to a peak value of -230.10
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kcal/mol (500-550 KDa) and there after a gradual decrease in dock scores were observed with
simultaneous increase in molecular weights of chalcones. This suggests the fact that the chalcones
with molecular weights more than 500 KDa are detrimental and hence these represent novel
scaffolds for ligand design and development.
Table 3: Molecular weight based comparison of designed chalcones with dock scores in kcal/mol
against PPAR gamma enzyme 2Q6S
MW
400-450
MW
450-500
MW
500-550
MW
550-600
MW
600-650
MW
650-700
MW
>700
12_14
(-189.27)
12_23
(-159.42)
14_23
(-165.29)
23_37
(-195.96)
13_19
(-215.72)
13_45
(-199.90)
13_36
(-195.59)
12_15
(-180.06)
12_29
(-197.97)
14_29
(-176.04)
23_49
(-183.95)
13_27
(-206.28)
19_36
(-202.98)
13_42
(-182.04)
12_17
(-171.72)
12_37
(-204.08)
14_37
(-194.69)
37_49
(-226.36)
13_43
(-192.52)
19_42
(-195.69)
36_42
(-165.28)
12_49
(-178.01)
14_49
(-175.31)
19_27
(-194.99)
19_45
(-225.38)
27_43
(-189.31)
14_15
(-173.40)
15_17
(-170.50)
19_43
(-204.15)
36_43
(-166.88)
27_45(-180.16)
14_17(-158.05)
15_37
(-198.58)
42_45
(-212.72)
36_45
(-166.56)
29_23(-162.41)
17_29
(-187.59)
43_49
(-168.05)
29_37(-190.73)
17_37
(-230.10)
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29_49(-173.98)
17_49
(-177.33)
42_43
(-183.07)
MW represents Molecular Weight and values in parentheses indicate molecular dock scores
(kcal/mol) of compounds against PPAR gamma. Benzaldehyde and acetophenone ids are separated
by underscore ‘_’.
Table 4: Top seven best chalcone 2D structures obtained from 2500 total data set.
O
O
F
F
O
NN
F
12_14
N
O
N
NN
NN
O
O
F
F
12_37
N
O
N
NN
NN
O
17_37
O
Cl
O
F
O
N
N
37_49
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O
B r
O
N
N
N
NN
19_45
N
O
Br
O
N N
N
Cl
19_36
N
O
N
N
BrO
N
N
C l
N
13_36
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Figure 1: Best chalcone id 17_37 displaying 11 H-bond interactions with active site residues of PPAR gamma 2Q6S.
In the next step, H-bond interactions of best dock scores in each molecular weight category
are tabulated and the geometric orientations within the active site region of PPAR gamma are
compared. Interestingly, 17_37 chalcone showed maximum number of interactions (11 H-bonds)
with Arg288, Ser342 and Ser289 of PPAR gamma. Other chalcones, given in Table 5, displayed
only a minimum of one to five H-bond interactions. Moreover, 2Q6S bound ligand glitazone formed
two H-bonds with His266 and Ser342 but reported a low affinity than 17_37 chalcone (-159.57 Vs -
230.10 kcal/mol). It has also been observed from Table 5 that most of the chalcones displayed H-
bonds with Arg288 residue. This is in agreement with other reports which have indicated that a high
affinity towards partial agonism was observed with various ligands.36,38 Literature search on similar
aspects revealed H-bond interactions with Cys285, Arg288 and Ser342 in 5(2-pyrimidinyloxy) 2-
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benzoylaminobenzoic acids36,37, whereas interactions with Arg288 has been observed in 1-O-
octadecenyl-2-hydroxy-sn-glycero-3-phosphate, which is a high affinity partial agonist.38
Table 5: H-bond interacting residues and dock scores of best chalcones from each molecular weight category.
S. No. Ligand No. of
interactions Residues Atom
MolDock Score
(kcal/mol)
1. 2Q6S bound
ligand 2
His266
Ser342
NE2
N
-159.57
2. 12_14 1 Cys285 SG -189.27
3. 12_37 5
His266
Ser342
Arg288
NE2
N
NE
-204.08
4. 17_37 11
Arg288
Ser342
Ser289
NH2, NE
N
OG
-230.10
5. 37_49 4
Ser342
His266
Cys285
Ser289
N
NE2
SG
OG
-226.36
6. 19_45 4 Cys285
Arg288
SG
NH2 -225.38
7. 19_36 2 Arg288 NH2 -202.98
8. 13_36 5
Arg288
Glu343
Ser342
NH2, NE
N
N
-195.59
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4. Conclusion: Screening of 2500 chalcones as possible PPAR gamma agonists resulted in seven
best compounds with dock scores far better than the original 2Q6S ligand. Molecular weight based
comparison of chalcones resulted in compound 17_37 with dock score -230.10 kcal/mol and a
maximum of 11 H-bond interactions suggest that accurate predictions can be achieved with few
computational efforts in a relatively short time and experimental evaluation of their biological
activities would help in designing compounds based on computer-aided techniques.
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Current Author Address:
Archana Kumari. D. N. S. S* Avanthi Institute of Pharmaceutical Sciences, Cherukupally (V), Bhogapauram (M), Near Tagarapuvalasa Bridge, Vizianagaram - 531162, India. Tel: +91-9394509950.